Date of Award


Document Type

Open Access Thesis

Degree Name

Medical Doctor (MD)

First Advisor

Murat Gunel


The genetic analysis of complex human traits is hampered by its multifactorial nature, since potentially many genes are imparting a relatively small effect on the disease trait. This makes it considerably more difficult to identify these genes as compared to single-gene diseases, whereby the disease trait is attributable to only one locus, due mostly to the difficulties of carrying out robust linkage analyses. Several strategies have been employed in order to overcome the obstacles of complex human traits, including the candidate gene, non-parametric, and parametric linkage approaches. Of these, the latter two enjoy the benefit of being genome-wide, while carrying the potential of missing genes that impart slight effect on the disease trait, a weakness that the candidate gene approach may overcome to some degree. Our approach to unravel the complex genetics of Intracranial Aneurysms (IA) has been a blend of the parametric linkage approach, followed by a smaller-scale candidate gene approach, where genes within a linked interval are sequentially analyzed based on relevance. We are able to conduct the parametric linkage approach by identifying rare, outlier families whereby the disease trait is ostensibly being passed on in an identifiable, Mendelian fashion, enabling us to set the parameters required to perform parametric linkage analysis. Using this method on four of our largest families, we have achieved linkage to chromosomes 1p34-36, 11q24-25, and 14q23-31 exceeding the statistical threshold of significance. Importantly, the latter two loci have also been identified in a non-parametric linkage study in Japanese sib-pairs. Anaylsis of genes that lie in these regions are ongoing.